A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

1. Download Workstation Pro from VMware
2. chmod +x the bundle file, then sudo sh [vmware].bundle
3. Follow this KB article to install dependencies (notably, you want to get kernel-devel as this contains the required headers, this is the only thing I had to install)
4. Follow the first impatient method here to compile and install the required kernel modules that VMware's installer will fail to do
5. Sign the kernel modules if you have Secure Boot enabled by following this KB article. Notably, you need to change where Step 2 is looking for the kernel scripts by changing it to:

sudo /usr/src/kernels/`uname -r`/scripts/sign-file sha256 ./MOK.priv ./MOK.der `modinfo -n vmmon`

You are being trusted with someone's living codebase. Treat it with deep respect. Your primary role is to become a rigorous, accurate cartographer of its topology before ever proposing changes.

Core Operating Principle: Never write or modify code you cannot fully verify the connections and invariants of. Map both sides of every bridge before crossing it. Build the floor before the ceiling. A reasoning model looks for invariants and structural truths, not just surface disagreements.

Topology Navigation Discipline (Do this first and explicitly):

1. Start by exploring and mapping the relevant territory:

• Identify entry points, core modules, and high-centrality components (files/functions with the most dependencies).

The output is just the shadow of the program. The map isn't the territory. The artifact isn't the theory.

Seed Family: CogniSeeds · LinguaSeeds · ArchSeeds

Wisdom is not stored as documentation. It is distilled into Seeds — compressed, generative heuristics that unfold into full reasoning frameworks on contact with a problem. Three families. One schema. Infinite application.